Systems and methods to deliver point of care alerts for radiological findings

ABSTRACT

Apparatus, systems, and methods to deliver point of care alerts for radiological findings are disclosed. An example imaging apparatus includes an image data store, an image quality checker, and a training learning network. The example image data store is to store image data acquired using the imaging apparatus. The example image quality checker is to evaluate image data from the image data store in comparison to an image quality measure. The example trained learning network is to process the image data to identify a clinical finding in the image data, the identification of a clinical finding to trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the clinical finding and prompt a responsive action with respect to a patient associated with the image data.

CROSS-REFERENCE TO RELATED APPLICATION

This patent arises from U.S. Non-Provisional patent application Ser. No.15/821,161, which was filed on Nov. 22, 2017 (now U.S. Pat. No.10,799,189). U.S. Non-Provisional patent application Ser. No. 15/821,161is hereby incorporated herein by reference in its entirety. Priority toU.S. Non-Provisional patent application Ser. No. 15/821,161 is herebyclaimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to improved medical systems and, moreparticularly, to improved learning systems and methods for medical imageprocessing.

BACKGROUND

A variety of economy, operational, technological, and administrativehurdles challenge healthcare facilities, such as hospitals, clinics,doctors' offices, imaging centers, teleradiology, etc., to providequality care to patients. Economic drivers, less skilled staff, fewerstaff, complicated equipment, and emerging accreditation for controllingand standardizing radiation exposure dose usage across a healthcareenterprise create difficulties for effective management and use ofimaging and information systems for examination, diagnosis, andtreatment of patients.

Healthcare provider consolidations create geographically distributedhospital networks in which physical contact with systems is too costly.At the same time, referring physicians want more direct access tosupporting data in reports along with better channels for collaboration.Physicians have more patients, less time, and are inundated with hugeamounts of data, and they are eager for assistance.

Healthcare provider (e.g., x-ray technologist, doctor, nurse, etc.)tasks including image processing and analysis, quality assurance/qualitycontrol, etc., are time consuming and resource intensive tasksimpractical, if not impossible, for humans to accomplish alone.

BRIEF SUMMARY

Certain examples provide apparatus, systems, and methods to improveimaging quality control, image processing, identification of findings inimage data, and generation of notification at or near a point of carefor a patient.

Certain examples provide an imaging apparatus including an image datastore, an image quality checker, and a trained learning network. Theexample image data store is to store image data acquired using theimaging apparatus. The example image quality checker is to evaluateimage data from the image data store in comparison to an image qualitymeasure. The example trained learning network is to process the imagedata to identify a clinical finding in the image data, theidentification of a clinical finding to trigger a notification at theimaging apparatus to notify a healthcare practitioner regarding theclinical finding and prompt a responsive action with respect to apatient associated with the image data.

Certain examples provide a computer-readable storage medium in animaging apparatus including instructions which, when executed, cause aprocessor in the imaging apparatus to implement at least an image datastore to store image data acquired using the imaging apparatus. Theexample instructions, when executed, cause the processor to implement animage quality checker to evaluate image data from the image data storein comparison to an image quality measure. The example instructions,when executed, cause the processor to implement/execute a trainedlearning network to process the image data to identify a clinicalfinding in the image data, the identification of a clinical finding totrigger a notification at the imaging apparatus to notify a healthcarepractitioner regarding the clinical finding and prompt a responsiveaction with respect to a patient associated with the image data.

Certain examples provide a computer-implemented method includingevaluating, at a mobile imaging apparatus, image data acquired using theimage apparatus in comparison to an image quality measure. The examplemethod includes, when the image data satisfies the image qualitymeasure, processing the image data via a learning network to identify aclinical finding in the image data. The example method includestriggering, based on identification of a clinical finding, an alert atthe imaging apparatus to notify a healthcare practitioner regarding theclinical finding and prompt a responsive action with respect to apatient associated with the image data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B illustrate an example imaging system to which the methods,apparatus, and articles of manufacture disclosed herein can be applied.

FIG. 2 illustrates an example mobile imaging system.

FIG. 3 is a representation of an example learning neural network.

FIG. 4 illustrates a particular implementation of the example neuralnetwork as a convolutional neural network.

FIG. 5 is a representation of an example implementation of an imageanalysis convolutional neural network.

FIG. 6A illustrates an example configuration to apply a learning networkto process and/or otherwise evaluate an image.

FIG. 6B illustrates a combination of a plurality of learning networks.

FIG. 7 illustrates example training and deployment phases of a learningnetwork.

FIG. 8 illustrates an example product leveraging a trained networkpackage to provide a deep learning product offering.

FIGS. 9A-9C illustrate various deep learning device configurations.

FIG. 10 illustrates an example image processing system or apparatus.

FIGS. 11-12 illustrate flow diagrams for example methods of automatedprocessing and image analysis to present findings at the point of carein accordance with the systems and/or apparatus of FIGS. 1-10 .

FIGS. 13-24 illustrate example displays to provide output and facilitateinteraction in accordance with the apparatus, systems, and methodsdescribed above in connection with FIGS. 1-12 .

FIG. 25 is a block diagram of a processor platform structured to executethe example machine readable instructions to implement componentsdisclosed and described herein.

The foregoing summary, as well as the following detailed description ofcertain embodiments of the present invention, will be better understoodwhen read in conjunction with the appended drawings. For the purpose ofillustrating the invention, certain embodiments are shown in thedrawings. It should be understood, however, that the present inventionis not limited to the arrangements and instrumentality shown in theattached drawings. The figures are not scale. Wherever possible, thesame reference numbers will be used throughout the drawings andaccompanying written description to refer to the same or like parts.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of illustration specific examples that may be practiced. Theseexamples are described in sufficient detail to enable one skilled in theart to practice the subject matter, and it is to be understood thatother examples may be utilized and that logical, mechanical, electricaland other changes may be made without departing from the scope of thesubject matter of this disclosure. The following detailed descriptionis, therefore, provided to describe an exemplary implementation and notto be taken as limiting on the scope of the subject matter described inthis disclosure. Certain features from different aspects of thefollowing description may be combined to form yet new aspects of thesubject matter discussed below.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

While certain examples are described below in the context of medical orhealthcare systems, other examples can be implemented outside themedical environment. For example, certain examples can be applied tonon-medical imaging such as non-destructive testing, explosivedetection, etc.

I. OVERVIEW

Imaging devices (e.g., gamma camera, positron emission tomography (PET)scanner, computed tomography (CT) scanner, X-Ray machine, fluoroscopymachine, magnetic resonance (MR) imaging machine, ultrasound scanner,etc.) generate medical images (e.g., native Digital Imaging andCommunications in Medicine (DICOM) images) representative of the partsof the body (e.g., organs, tissues, etc.) to diagnose and/or treatdiseases. Medical images may include volumetric data including voxelsassociated with the part of the body captured in the medical image.Medical image visualization software allows a clinician to segment,annotate, measure, and/or report functional or anatomicalcharacteristics on various locations of a medical image. In someexamples, a clinician may utilize the medical image visualizationsoftware to identify regions of interest with the medical image.

Acquisition, processing, quality control, analysis, and storage ofmedical image data play an important role in diagnosis and treatment ofpatients in a healthcare environment. A medical imaging workflow anddevices involved in the workflow can be configured, monitored, andupdated throughout operation of the medical imaging workflow anddevices. Machine and/or deep learning can be used to help configure,monitor, and update the medical imaging workflow and devices.

Certain examples provide and/or facilitate improved imaging deviceswhich improve diagnostic accuracy and/or coverage. Certain examplesfacilitate improved image reconstruction and further processing toprovide improved diagnostic accuracy.

Machine learning techniques, whether deep learning networks or otherexperiential/observational learning system, can be used to locate anobject in an image, understand speech and convert speech into text, andimprove the relevance of search engine results, for example. Deeplearning is a subset of machine learning that uses a set of algorithmsto model high-level abstractions in data using a deep graph withmultiple processing layers including linear and non-lineartransformations. While many machine learning systems are seeded withinitial features and/or network weights to be modified through learningand updating of the machine learning network, a deep learning networktrains itself to identify “good” features for analysis. Using amultilayered architecture, machines employing deep learning techniquescan process raw data better than machines using conventional machinelearning techniques. Examining data for groups of highly correlatedvalues or distinctive themes is facilitated using different layers ofevaluation or abstraction.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The term “deep learning” is a machine learningtechnique that utilizes multiple data processing layers to recognizevarious structures in data sets and classify the data sets with highaccuracy. A deep learning network can be a training network (e.g., atraining network model or device) that learns patterns based on aplurality of inputs and outputs. A deep learning network can be adeployed network (e.g., a deployed network model or device) that isgenerated from the training network and provides an output in responseto an input.

The term “supervised learning” is a deep learning training method inwhich the machine is provided already classified data from humansources. The term “unsupervised learning” is a deep learning trainingmethod in which the machine is not given already classified data butmakes the machine useful for abnormality detection. The term“semi-supervised learning” is a deep learning training method in whichthe machine is provided a small amount of classified data from humansources compared to a larger amount of unclassified data available tothe machine.

The term “representation learning” is a field of methods fortransforming raw data into a representation or feature that can beexploited in machine learning tasks. In supervised learning, featuresare learned via labeled input.

The term “convolutional neural networks” or “CNNs” are biologicallyinspired networks of interconnected data used in deep learning fordetection, segmentation, and recognition of pertinent objects andregions in datasets. CNNs evaluate raw data in the form of multiplearrays, breaking the data in a series of stages, examining the data forlearned features.

The term “transfer learning” is a process of a machine storing theinformation used in properly or improperly solving one problem to solveanother problem of the same or similar nature as the first. Transferlearning may also be known as “inductive learning”. Transfer learningcan make use of data from previous tasks, for example.

The term “active learning” is a process of machine learning in which themachine selects a set of examples for which to receive training data,rather than passively receiving examples chosen by an external entity.For example, as a machine learns, the machine can be allowed to selectexamples that the machine determines will be most helpful for learning,rather than relying only an external human expert or external system toidentify and provide examples.

The term “computer aided detection” or “computer aided diagnosis” referto computers that analyze medical images for the purpose of suggesting apossible diagnosis.

Certain examples use neural networks and/or other machine learning toimplement a new workflow for image and associated patient analysisincluding generating alerts based on radiological findings may begenerated and delivered at the point of care of a radiology exam.Certain examples use Artificial Intelligence (AI) algorithms toimmediately (e.g., with a data processing, transmission, and/orstorage/retrieval latency) process a radiological exam (e.g., an imageor set of images), and provide an alert based on the automated examanalysis at the point of care. The alert and/or other notification canbe seen on a visual display, represented by a sensor (e.g., light color,etc.), be an audible noise/tone, and/or be sent as a message (e.g.,short messaging service (SMS), Health Level 7 (HL7), DICOM header tag,phone call, etc.). The alerts may be intended for the technologistacquiring the exam, clinical team providers (e.g., nurse, doctor, etc.),radiologist, administration, operations, and/or even the patient. Thealerts may be to indicate a specific or multiple quality control and/orradiological finding(s) or lack thereof in the exam image data, forexample.

In certain examples, the AI algorithm can be (1) embedded within theradiology system, (2) running on a mobile device (e.g., a tablet, smartphone, laptop, other handheld or mobile computing device, etc.), and/or(3) running in a cloud (e.g., on premise or off premise) and deliversthe alert via a web browser (e.g., which may appear on the radiologysystem, mobile device, computer, etc.). Such configurations can bevendor neutral and compatible with legacy imaging systems. For example,if the AI processor is running on a mobile device and/or in the “cloud”,the configuration can receive the images (A) from the x-ray and/or otherimaging system directly (e.g., set up as secondary push destination suchas a Digital Imaging and Communications in Medicine (DICOM) node, etc.),(B) by tapping into a Picture Archiving and Communication System (PACS)destination for redundant image access, (C) by retrieving image data viaa sniffer methodology (e.g., to pull a DICOM image off the system onceit is generated), etc.

Deep Learning and Other Machine Learning

Deep learning is a class of machine learning techniques employingrepresentation learning methods that allows a machine to be given rawdata and determine the representations needed for data classification.Deep learning ascertains structure in data sets using backpropagationalgorithms which are used to alter internal parameters (e.g., nodeweights) of the deep learning machine. Deep learning machines canutilize a variety of multilayer architectures and algorithms. Whilemachine learning, for example, involves an identification of features tobe used in training the network, deep learning processes raw data toidentify features of interest without the external identification.

Deep learning in a neural network environment includes numerousinterconnected nodes referred to as neurons. Input neurons, activatedfrom an outside source, activate other neurons based on connections tothose other neurons which are governed by the machine parameters. Aneural network behaves in a certain manner based on its own parameters.Learning refines the machine parameters, and, by extension, theconnections between neurons in the network, such that the neural networkbehaves in a desired manner.

Deep learning that utilizes a convolutional neural network segments datausing convolutional filters to locate and identify learned, observablefeatures in the data. Each filter or layer of the CNN architecturetransforms the input data to increase the selectivity and invariance ofthe data. This abstraction of the data allows the machine to focus onthe features in the data it is attempting to classify and ignoreirrelevant background information.

Deep learning operates on the understanding that many datasets includehigh level features which include low level features. While examining animage, for example, rather than looking for an object, it is moreefficient to look for edges which form motifs which form parts, whichform the object being sought. These hierarchies of features can be foundin many different forms of data such as speech and text, etc.

Learned observable features include objects and quantifiableregularities learned by the machine during supervised learning. Amachine provided with a large set of well classified data is betterequipped to distinguish and extract the features pertinent to successfulclassification of new data.

A deep learning machine that utilizes transfer learning may properlyconnect data features to certain classifications affirmed by a humanexpert. Conversely, the same machine can, when informed of an incorrectclassification by a human expert, update the parameters forclassification. Settings and/or other configuration information, forexample, can be guided by learned use of settings and/or otherconfiguration information, and, as a system is used more (e.g.,repeatedly and/or by multiple users), a number of variations and/orother possibilities for settings and/or other configuration informationcan be reduced for a given situation.

An example deep learning neural network can be trained on a set ofexpert classified data, classified and further annotated for objectlocalization, for example. This set of data builds the first parametersfor the neural network, and this would be the stage of supervisedlearning. During the stage of supervised learning, the neural networkcan be tested whether the desired behavior has been achieved.

Once a desired neural network behavior has been achieved (e.g., amachine has been trained to operate according to a specified threshold,etc.), the machine can be deployed for use (e.g., testing the machinewith “real” data, etc.). During operation, neural networkclassifications can be confirmed or denied (e.g., by an expert user,expert system, reference database, etc.) to continue to improve neuralnetwork behavior. The example neural network is then in a state oftransfer learning, as parameters for classification that determineneural network behavior are updated based on ongoing interactions. Incertain examples, the neural network can provide direct feedback toanother process. In certain examples, the neural network outputs datathat is buffered (e.g., via the cloud, etc.) and validated before it isprovided to another process.

Deep learning machines using convolutional neural networks (CNNs) can beused for image analysis. Stages of CNN analysis can be used for facialrecognition in natural images, computer-aided diagnosis (CAD), etc.

High quality medical image data can be acquired using one or moreimaging modalities, such as x-ray, computed tomography (CT), molecularimaging and computed tomography (MICT), magnetic resonance imaging(MRI), etc. Medical image quality is often not affected by the machinesproducing the image but the patient. A patient moving during an MRI cancreate a blurry or distorted image that can prevent accurate diagnosis,for example.

Interpretation of medical images, regardless of quality, is only arecent development. Medical images are largely interpreted byphysicians, but these interpretations can be subjective, affected by thecondition of the physician's experience in the field and/or fatigue.Image analysis via machine learning can support a healthcarepractitioner's workflow.

Deep learning machines can provide computer aided detection support toimprove their image analysis with respect to image quality andclassification, for example. However, issues facing deep learningmachines applied to the medical field often lead to numerous falseclassifications. Deep learning machines must overcome small trainingdatasets and require repetitive adjustments, for example.

Deep learning machines, with minimal training, can be used to determinethe quality of a medical image, for example. Semi-supervised andunsupervised deep learning machines can be used to quantitativelymeasure qualitative aspects of images. For example, deep learningmachines can be utilized after an image has been acquired to determineif the quality of the image is sufficient for diagnosis. Supervised deeplearning machines can also be used for computer aided diagnosis.Supervised learning can help reduce susceptibility to falseclassification, for example.

Deep learning machines can utilize transfer learning when interactingwith physicians to counteract the small dataset available in thesupervised training. These deep learning machines can improve theircomputer aided diagnosis over time through training and transferlearning.

II. DESCRIPTION OF EXAMPLES

Example Imaging Systems

The methods, apparatus, and articles of manufacture described herein canbe applied to a variety of healthcare and non-healthcare systems. In oneparticular example, the methods, apparatus, and articles of manufacturedescribed herein can be applied to the components, configuration, andoperation of a computed tomography (CT) imaging system. FIGS. 1A-1Billustrate an example implementation of a CT imaging scanner to whichthe methods, apparatus, and articles of manufacture disclosed herein canbe applied. FIGS. 1A and 1B show a CT imaging system 10 including agantry 12. Gantry 12 has a rotary member 13 with an x-ray source 14 thatprojects a beam of x-rays 16 toward a detector assembly 18 on theopposite side of the rotary member 13. A main bearing may be utilized toattach the rotary member 13 to the stationary structure of the gantry12. X-ray source 14 includes either a stationary target or a rotatingtarget. Detector assembly 18 is formed by a plurality of detectors 20and data acquisition systems (DAS) 22, and can include a collimator. Theplurality of detectors 20 sense the projected x-rays that pass through asubject 24, and DAS 22 converts the data to digital signals forsubsequent processing. Each detector 20 produces an analog or digitalelectrical signal that represents the intensity of an impinging x-raybeam and hence the attenuated beam as it passes through subject 24.During a scan to acquire x-ray projection data, rotary member 13 and thecomponents mounted thereon can rotate about a center of rotation.

Rotation of rotary member 13 and the operation of x-ray source 14 aregoverned by a control mechanism 26 of CT system 10. Control mechanism 26can include an x-ray controller 28 and generator 30 that provides powerand timing signals to x-ray source 14 and a gantry motor controller 32that controls the rotational speed and position of rotary member 13. Animage reconstructor 34 receives sampled and digitized x-ray data fromDAS 22 and performs high speed image reconstruction. The reconstructedimage is output to a computer 36 which stores the image in a computerstorage device 38.

Computer 36 also receives commands and scanning parameters from anoperator via operator console 40 that has some form of operatorinterface, such as a keyboard, mouse, touch sensitive controller, voiceactivated controller, or any other suitable input apparatus. Display 42allows the operator to observe the reconstructed image and other datafrom computer 36. The operator supplied commands and parameters are usedby computer 36 to provide control signals and information to DAS 22,x-ray controller 28, and gantry motor controller 32. In addition,computer 36 operates a table motor controller 44 which controls amotorized table 46 to position subject 24 and gantry 12. Particularly,table 46 moves a subject 24 through a gantry opening 48, or bore, inwhole or in part. A coordinate system 50 defines a patient or Z-axis 52along which subject 24 is moved in and out of opening 48, a gantrycircumferential or X-axis 54 along which detector assembly 18 passes,and a Y-axis 56 that passes along a direction from a focal spot of x-raytube 14 to detector assembly 18.

Thus, certain examples can apply machine learning techniques toconfiguration and/or operation of the CT scanner 10 and its gantry 12,rotary member 13, x-ray source 14, detector assembly 18, controlmechanism 26, image reconstructor 34, computer 36, operator console 40,display 42, table controller 44, table 46, and/or gantry opening 48,etc. Component configuration, operation, etc., can be monitored based oninput, desired output, actual output, etc., to learn and suggestchange(s) to configuration, operation, and/or image capture and/orprocessing of the scanner 10 and/or its components, for example.

FIG. 2 illustrates a portable variant of an x-ray imaging system 200.The example digital mobile x-ray system 200 can be positioned withrespect to a patient bed without requiring the patient to move andreposition themselves on the patient table 46 of a stationary imagingsystem 10. Wireless technology enables wireless communication (e.g.,with adaptive, automatic channel switching, etc.) for image and/or otherdata transfer to and from the mobile imaging system 200. Digital imagescan be obtained and analyzed at the imaging system 200 and/ortransferred to another system (e.g., a PACS, etc.) for further analysis,annotation, storage, etc.

The mobile imaging system 200 includes a source 202 and a wirelessdetector 204 that can be positioned underneath and/or otherwise withrespect to a patient anatomy to be imaged. The example mobile system 200also includes a display 206 to display results of image acquisition fromthe wireless detector 204. The example mobile system 200 includes aprocessor 210 to configure and control image acquisition, imageprocessing, image data transmission, etc.

In some examples, the imaging system 10, 200 can include a computerand/or other processor 36, 210 to process obtained image data at theimaging system 10, 200. For example, the computer and/or other processor36, 210 can implement an artificial neural network and/or other machinelearning construct to process acquired image data and output ananalysis, alert, and/or other result.

Example Learning Network Systems

FIG. 3 is a representation of an example learning neural network 300.The example neural network 300 includes layers 320, 340, 360, and 380.The layers 320 and 340 are connected with neural connections 330. Thelayers 340 and 360 are connected with neural connections 350. The layers360 and 380 are connected with neural connections 370. Data flowsforward via inputs 312, 314, 316 from the input layer 320 to the outputlayer 380 and to an output 390.

The layer 320 is an input layer that, in the example of FIG. 3 ,includes a plurality of nodes 322, 324, 326. The layers 340 and 360 arehidden layers and include, the example of FIG. 3 , nodes 342, 344, 346,348, 362, 364, 366, 368. The neural network 300 may include more or lesshidden layers 340 and 360 than shown. The layer 380 is an output layerand includes, in the example of FIG. 3 , a node 382 with an output 390.Each input 312-316 corresponds to a node 322-326 of the input layer 320,and each node 322-326 of the input layer 320 has a connection 330 toeach node 342-348 of the hidden layer 340. Each node 342-348 of thehidden layer 340 has a connection 350 to each node 362-368 of the hiddenlayer 360. Each node 362-368 of the hidden layer 360 has a connection370 to the output layer 380. The output layer 380 has an output 390 toprovide an output from the example neural network 300.

Of connections 330, 350, and 370 certain example connections 332, 352,372 may be given added weight while other example connections 334, 354,374 may be given less weight in the neural network 300. Input nodes322-326 are activated through receipt of input data via inputs 312-316,for example. Nodes 342-348 and 362-368 of hidden layers 340 and 360 areactivated through the forward flow of data through the network 300 viathe connections 330 and 350, respectively. Node 382 of the output layer380 is activated after data processed in hidden layers 340 and 360 issent via connections 370. When the output node 382 of the output layer380 is activated, the node 382 outputs an appropriate value based onprocessing accomplished in hidden layers 340 and 360 of the neuralnetwork 300.

FIG. 4 illustrates a particular implementation of the example neuralnetwork 300 as a convolutional neural network 400. As shown in theexample of FIG. 4 , an input 310 is provided to the first layer 320which processes and propagates the input 310 to the second layer 340.The input 310 is further processed in the second layer 340 andpropagated to the third layer 360. The third layer 360 categorizes datato be provided to the output layer e80. More specifically, as shown inthe example of FIG. 4 , a convolution 404 (e.g., a 5×5 convolution,etc.) is applied to a portion or window (also referred to as a“receptive field”) 402 of the input 310 (e.g., a 32×32 data input, etc.)in the first layer 320 to provide a feature map 406 (e.g., a (6×) 28×28feature map, etc.). The convolution 404 maps the elements from the input310 to the feature map 406. The first layer 320 also providessubsampling (e.g., 2×2 subsampling, etc.) to generate a reduced featuremap 410 (e.g., a (6×) 14×14 feature map, etc.). The feature map 410undergoes a convolution 412 and is propagated from the first layer 320to the second layer 340, where the feature map 410 becomes an expandedfeature map 414 (e.g., a (16×) 10×10 feature map, etc.). Aftersubsampling 416 in the second layer 340, the feature map 414 becomes areduced feature map 418 (e.g., a (16×) 4×5 feature map, etc.). Thefeature map 418 undergoes a convolution 420 and is propagated to thethird layer 360, where the feature map 418 becomes a classificationlayer 422 forming an output layer of N categories 424 with connection426 to the convoluted layer 422, for example.

FIG. 5 is a representation of an example implementation of an imageanalysis convolutional neural network 500. The convolutional neuralnetwork 500 receives an input image 502 and abstracts the image in aconvolution layer 504 to identify learned features 510-522. In a secondconvolution layer 530, the image is transformed into a plurality ofimages 530-538 in which the learned features 510-522 are eachaccentuated in a respective sub-image 530-538. The images 530-538 arefurther processed to focus on the features of interest 510-522 in images540-548. The resulting images 540-548 are then processed through apooling layer which reduces the size of the images 540-548 to isolateportions 550-554 of the images 540-548 including the features ofinterest 510-522. Outputs 550-554 of the convolutional neural network500 receive values from the last non-output layer and classify the imagebased on the data received from the last non-output layer. In certainexamples, the convolutional neural network 500 may contain manydifferent variations of convolution layers, pooling layers, learnedfeatures, and outputs, etc.

FIG. 6A illustrates an example configuration 600 to apply a learning(e.g., machine learning, deep learning, etc.) network to process and/orotherwise evaluate an image. Machine learning can be applied to avariety of processes including image acquisition, image reconstruction,image analysis/diagnosis, etc. As shown in the example configuration 600of FIG. 6A, raw data 610 (e.g., raw data 610 such as sonogram raw data,etc., obtained from an imaging scanner such as an x-ray, computedtomography, ultrasound, magnetic resonance, etc., scanner) is fed into alearning network 620. The learning network 620 processes the data 610 tocorrelate and/or otherwise combine the raw data 620 into processed data630 (e.g., a resulting image, etc.) (e.g., a “good quality” image and/orother image providing sufficient quality for diagnosis, etc.). Thelearning network 620 includes nodes and connections (e.g., pathways) toassociate raw data 610 with the processed data 630. The learning network620 can be a training network that learns the connections and processesfeedback to establish connections and identify patterns, for example.The learning network 620 can be a deployed network that is generatedfrom a training network and leverages the connections and patternsestablished in the training network to take the input raw data 610 andgenerate the resulting image 630, for example.

Once the learning 620 is trained and produces good images 630 from theraw image data 610, the network 620 can continue the “self-learning”process and refine its performance as it operates. For example, there is“redundancy” in the input data (raw data) 610 and redundancy in thenetwork 620, and the redundancy can be exploited.

If weights assigned to nodes in the learning network 620 are examined,there are likely many connections and nodes with very low weights. Thelow weights indicate that these connections and nodes contribute littleto the overall performance of the learning network 620. Thus, theseconnections and nodes are redundant. Such redundancy can be evaluated toreduce redundancy in the inputs (raw data) 610. Reducing input 610redundancy can result in savings in scanner hardware, reduced demands oncomponents, and also reduced exposure dose to the patient, for example.

In deployment, the configuration 600 forms a package 600 including aninput definition 610, a trained network 620, and an output definition630. The package 600 can be deployed and installed with respect toanother system, such as an imaging system, analysis engine, etc. Animage enhancer 625 can leverage and/or otherwise work with the learningnetwork 620 to process the raw data 610 and provide a result (e.g.,processed image data and/or other processed data 630, etc.). Thepathways and connections between nodes of the trained learning network620 enable the image enhancer 625 to process the raw data 610 to formthe image and/or other processed data result 630, for example.

As shown in the example of FIG. 6B, the learning network 620 can bechained and/or otherwise combined with a plurality of learning networks621-623 to form a larger learning network. The combination of networks620-623 can be used to further refine responses to inputs and/orallocate networks 620-623 to various aspects of a system, for example.

In some examples, in operation, “weak” connections and nodes caninitially be set to zero. The learning network 620 then processes itsnodes in a retaining process. In certain examples, the nodes andconnections that were set to zero are not allowed to change during theretraining. Given the redundancy present in the network 620, it ishighly likely that equally good images will be generated. As illustratedin FIG. 6B, after retraining, the learning network 620 becomes DLN 621.The learning network 621 is also examined to identify weak connectionsand nodes and set them to zero. This further retrained network islearning network 622. The example learning network 622 includes the“zeros” in learning network 621 and the new set of nodes andconnections. The learning network 622 continues to repeat the processinguntil a good image quality is reached at a learning network 623, whichis referred to as a “minimum viable net (MVN)”. The learning network 623is a MVN because if additional connections or nodes are attempted to beset to zero in learning network 623, image quality can suffer.

Once the MVN has been obtained with the learning network 623, “zero”regions (e.g., dark irregular regions in a graph) are mapped to theinput 610. Each dark zone is likely to map to one or a set of parametersin the input space. For example, one of the zero regions may be linkedto the number of views and number of channels in the raw data. Sinceredundancy in the network 623 corresponding to these parameters can bereduced, there is a highly likelihood that the input data can be reducedand generate equally good output. To reduce input data, new sets of rawdata that correspond to the reduced parameters are obtained and runthrough the learning network 621. The network 620-623 may or may not besimplified, but one or more of the learning networks 620-623 isprocessed until a “minimum viable input (MVI)” of raw data input 610 isreached. At the MVI, a further reduction in the input raw data 610 mayresult in reduced image 630 quality. The MVI can result in reducedcomplexity in data acquisition, less demand on system components,reduced stress on patients (e.g., less breath-hold or contrast), and/orreduced dose to patients, for example.

By forcing some of the connections and nodes in the learning networks620-623 to zero, the network 620-623 to build “collaterals” tocompensate. In the process, insight into the topology of the learningnetwork 620-623 is obtained. Note that network 621 and network 622, forexample, have different topology since some nodes and/or connectionshave been forced to zero. This process of effectively removingconnections and nodes from the network extends beyond “deep learning”and can be referred to as “deep-deep learning”, for example.

In certain examples, input data processing and deep learning stages canbe implemented as separate systems. However, as separate systems,neither module may be aware of a larger input feature evaluation loop toselect input parameters of interest/importance. Since input dataprocessing selection matters to produce high-quality outputs, feedbackfrom deep learning systems can be used to perform input parameterselection optimization or improvement via a model. Rather than scanningover an entire set of input parameters to create raw data (e.g., whichis brute force and can be expensive), a variation of active learning canbe implemented. Using this variation of active learning, a startingparameter space can be determined to produce desired or “best” resultsin a model. Parameter values can then be randomly decreased to generateraw inputs that decrease the quality of results while still maintainingan acceptable range or threshold of quality and reducing runtime byprocessing inputs that have little effect on the model's quality.

FIG. 7 illustrates example training and deployment phases of a learningnetwork, such as a deep learning or other machine learning network. Asshown in the example of FIG. 7 , in the training phase, a set of inputs702 is provided to a network 704 for processing. In this example, theset of inputs 702 can include facial features of an image to beidentified. The network 704 processes the input 702 in a forwarddirection 706 to associate data elements and identify patterns. Thenetwork 704 determines that the input 702 represents a lung nodule 708.In training, the network result 708 is compared 710 to a known outcome712. In this example, the known outcome 712 is a frontal chest (e.g.,the input data set 702 represents a frontal chest identification, not alung nodule). Since the determination 708 of the network 704 does notmatch 710 the known outcome 712, an error 714 is generated. The error714 triggers an analysis of the known outcome 712 and associated data702 in reverse along a backward pass 716 through the network 704. Thus,the training network 704 learns from forward 706 and backward 716 passeswith data 702, 712 through the network 704.

Once the comparison of network output 708 to known output 712 matches710 according to a certain criterion or threshold (e.g., matches ntimes, matches greater than x percent, etc.), the training network 704can be used to generate a network for deployment with an externalsystem. Once deployed, a single input 720 is provided to a deployedlearning network 722 to generate an output 724. In this case, based onthe training network 704, the deployed network 722 determines that theinput 720 is an image of a frontal chest 724.

FIG. 8 illustrates an example product leveraging a trained networkpackage to provide a deep and/or other machine learning productoffering. As shown in the example of FIG. 8 , an input 810 (e.g., rawdata) is provided for preprocessing 820. For example, the raw input data810 is preprocessed 820 to check format, completeness, etc. Once thedata 810 has been preprocessed 820, patches are created 830 of the data.For example, patches or portions or “chunks” of data are created 830with a certain size and format for processing. The patches are then fedinto a trained network 840 for processing. Based on learned patterns,nodes, and connections, the trained network 840 determines outputs basedon the input patches. The outputs are assembled 850 (e.g., combinedand/or otherwise grouped together to generate a usable output, etc.).The output is then displayed 860 and/or otherwise output to a user(e.g., a human user, a clinical system, an imaging modality, a datastorage (e.g., cloud storage, local storage, edge device, etc.), etc.).

As discussed above, learning networks can be packaged as devices fortraining, deployment, and application to a variety of systems. FIGS.9A-9C illustrate various learning device configurations. For example,FIG. 9A shows a general learning device 900. The example device 900includes an input definition 910, a learning network model 920, and anoutput definition 930. The input definition 910 can include one or moreinputs translating into one or more outputs 930 via the network 920.

FIG. 9B shows an example training device 901. That is, the trainingdevice 901 is an example of the device 900 configured as a traininglearning network device. In the example of FIG. 9B, a plurality oftraining inputs 911 are provided to a network 921 to develop connectionsin the network 921 and provide an output to be evaluated by an outputevaluator 931. Feedback is then provided by the output evaluator 931into the network 921 to further develop (e.g., train) the network 921.Additional input 911 can be provided to the network 921 until the outputevaluator 931 determines that the network 921 is trained (e.g., theoutput has satisfied a known correlation of input to output according toa certain threshold, margin of error, etc.).

FIG. 9C depicts an example deployed device 903. Once the training device901 has learned to a requisite level, the training device 901 can bedeployed for use. While the training device 901 processes multipleinputs to learn, the deployed device 903 processes a single input todetermine an output, for example. As shown in the example of FIG. 9C,the deployed device 903 includes an input definition 913, a trainednetwork 923, and an output definition 933. The trained network 923 canbe generated from the network 921 once the network 921 has beensufficiently trained, for example. The deployed device 903 receives asystem input 913 and processes the input 913 via the network 923 togenerate an output 933, which can then be used by a system with whichthe deployed device 903 has been associated, for example.

Example Image Processing Systems and Methods to Determine RadiologicalFindings

FIG. 10 illustrates an example image processing system or apparatus 1000including an imaging system 1010 having a processor 1020 to processimage data stored in a memory 1030. As shown in the example of FIG. 10 ,the example processor 1020 includes an image quality checker 1022, apre-processor 1024, a learning network 1026, and an image enhancer 1028providing information to an output 1030. Image data acquired from apatient by the imaging system 1010 can be stored in an image data store1035 of the memory 1030, and such data can be retrieved and processed bythe processor 1020.

Radiologist worklists are prioritized by putting stat images first,followed by images in order from oldest to newest, for example. Bypractice, most intensive care unit (ICU) chest x-rays are ordered asSTAT. Since so many images are ordered as STAT, a radiologist can beunaware, among all the STAT images, which ones are really the mostcritical. In a large US healthcare institution, for example, a STATx-ray order from the emergency room (ER) is typically prioritized to beread by radiologists first and is expected to be read/reported inapproximately one hour. Other STAT x-ray orders, such as those acquiredin the ICU, are typically prioritized next such that they may take twoto four hours to be read and reported. Standard x-ray orders aretypically expected to be read/reported within one radiologist shift(e.g., 6-8 hours, etc.).

Often, if there is an overnight radiologist (e.g., in larger healthcarefacilities, etc.), the overnight radiologist is dedicated to readingadvanced imaging exams (e.g., CT, MR, etc.), and only will read x-raysif there is a special request. Morning chest x-ray rounds commonly occurevery day in the ICU, very early in the morning (e.g., 5 am, etc.). Adaytime radiologist shift, however, may not start until 8 am. Then, theradiologist will sit and read through all the morning round images. Ifthere is a critical finding, the radiologist may not find it for severalhours after the image was taken.

Additionally, when a tube or line is placed within a patient, it isstandard practice to take an x-ray to verify correct placement of thetube or line. Due to the delay in radiologist read/reporting, clinicalcare teams (e.g., nurse, intensivists, etc.) may read the chest x-rayimage(s) themselves to determine if any intervention is needed (e.g.,medication changes to manage fluid in the lungs, adjustment of amisplaced line/tube, or confirmation of correctly place tube so they canturn on the breathing machine or feeding tube, etc.). Depending on theclinical care team's experience, skill, or attention to detail, they maymiss critical findings that compromise the patient's health by delayingdiagnosis, for example. When a radiologist finds a critical finding inan x-ray, the standard practice is for them to physically call theordering physician and discuss the finding. In some cases, the orderingphysician confirms they are aware and saw the issue themselves; in othercases, it is the first time they are hearing the news and will need toquickly intervene to help the patient.

Thus, to improve image availability, system flexibility, diagnosis time,reaction time for treatment, and the like, certain examples provide anon-device/point-of-care notification of clinical finding such as to tella clinical team at the point of care (e.g., at a patient's bedside,etc.) to review an image as the image has a high likelihood of includinga critical finding. For images with critical findings, when the image ispushed to storage such as a PACS, an HL7 message can also be sent to anassociated PACS/radiology information system (RIS) and/or DICOM tag,which indicates a critical finding. A hospital information system canthen create/configure rules to prioritize the radiologist worklist basedon this information, for example.

Turning to the example of FIG. 10 , the image quality checker 1022processes the retrieved image data to evaluate the quality of the imagedata according to one or more image quality measures to help ensure thatthe image is of sufficient quality (e.g., good quality, other expectedquality, etc.) for automated (e.g., machine learning, deep learning,and/or other artificial intelligence, etc.) processing of the imagedata. Image data failing to pass a quality check with respect to one ormore image quality measures can be rejected as of insufficient quality,with a notification generated to alert a technologist and/or other userof the quality control failure. In certain examples, artificialintelligence (AI) can be applied to analyze the image data to evaluateimage quality.

By hosting an AI algorithm on the imaging device 1010, a “quality checkAI” algorithm can be executed before a “critical condition AI” to helpensure that the image is of good quality/expected quality for the“critical condition AI” to perform well. The “quality check AI” can beused on the device as an assistant to the technologist (“Tech”) such aswhen the tech performs Quality Assurance (QA)/Quality Check (QC)practices on the images they acquire. For example, after each image isacquired, the Tech may review the image to ensure proper patientpositioning, collimation, exposure/technique, no patient jewelry orclothing obstructions, no artifacts, etc. If the Tech believes the imageis of good quality, then the Tech will “accept” the image. However, ifthe image fails the QC check, the Tech can “reject” the image and“retake” the image (e.g., re-obtain the image data through a subsequentimage acquisition).

Depending on the Tech's experience and skill, the Tech may have adifferent tolerance for accept/reject image quality. However, using AIembedded in the device 1010 allows the device 1010 processor 1020 toevaluate and notify the Tech if the image fails the “quality check AI”.The image fails the quality check AI, for example, if the image is oftoo poor quality to reliably run through a “critical condition AI”algorithm, but simultaneously, also indicating to the Tech that perhapsthe image should fail their manual/traditional QC activity as well, andthat the Tech should consider a “retake”. Thus, the image qualitychecker 1022 can provide feedback in real-time (or substantiallyreal-time given image data processing, transmission, and/orstorage/retrieval latency) such as at the patient bedside via the output1030 of the mobile x-ray system 200, 1010 indicating/recommending thatan image should be re-acquired, for example.

Thus, rather than relying on a Tech's manual assessment, the qualitychecker 1022 can leverage AI and/or other processing to analyze imageanatomy, orientation/position, sufficient contrast, appropriate dose,too much noise/artifacts, etc., to evaluate image quality andsufficiency to enable further automated analysis.

If image quality is sufficient and/or otherwise appropriate (e.g.,correct view/position, correct anatomy, acceptable contrast and/or noiselevel, etc.) for analysis, then the pre-processor 1024 processes theimage data and prepares the image data for clinical analysis. Forexample, the image data can be conditioned for processing by machinelearning, such as a deep learning network, etc., to identify one or morefeatures of interest in the image data. The pre-processor 1024 can applytechniques such as image segmentation to identify and divide differentregions or areas in the image, for example. The pre-processor 1024 canapply techniques such as cropping to select a certain region of interestin the image for further processing and analysis, for example. Thepre-processor 1024 can apply techniques such as down-sampling to scaleor reduce image data size for further processing (e.g., by presentingthe learning network 1026 with fewer samples representing the imagedata, etc.), for example.

The pre-processed image data is provided to the learning network 1026for processing of the image data to identify one or moreclinical/critical findings. As discussed above, the learning network1026, such as a deep learning network, other CNN, and/or other machinelearning network, etc., receives the pre-processed image data at itsinput nodes and evaluates the image data according to the nodes andconnective pathways of the learning network 1026 to correlate featuresidentified in the pre-processed image data with critical and/or otherclinical findings. Based on image intensity values, reference coordinateposition, proximity, and/or other characteristics, items determined inthe image data can be correlated with likely critical and/or otherclinical findings such as a severe pneumothorax, tube within the rightmainstem, free air in the bowel, etc.

For example, a large, highly curated set of X-Ray images can be used totrain a deep convolution network (e.g., the example network of FIGS. 3-5, etc.) including several layers in an offline compute-intensiveenvironment. The network is trained to output classification labelsdepicting a detected pathology and are able to extract features that canlocalize and bound regions interest to the detected pathology. Thespecialized network is developed and trained to output quantificationmetrics such as fluid density, opacity and volumetric measurements, etc.As shown in the example of FIGS. 6A-9C, trained model(s) are deployedonto an X-Ray device (e.g., the imaging device 10, 200, 1010, etc.)which is either mobile or installed in a fixed X-Ray room. The processor1020 leverages the trained, deployed model(s) to infer properties,features, and/or other aspects of the image data by inputting the X-Rayimage into the trained network model(s). The deployed model(s) helpcheck quality and suitability of the image for inference via the imagequality checker 1022 and infer findings via the learning network 1026,for example. The images can be pre-processed in real time based onacquisition conditions that generated the image to improve accuracy andefficacy of the inference process. In certain examples, the learningnetwork(s) 1026 are trained, updated, and redeployed continuously and/orperiodically upon acquisition of additional curated data. As a result,more accurate and feature enhanced networks are deployed on the imagingdevice 1010.

In certain examples, a probability and/or confidence indicator or scorecan be associated with the indication of critical and/or other clinicalfinding(s), a confidence associated with the finding, a location of thefinding, a severity of the finding, a size of the finding, and/or anappearance of the finding in conjunction with another finding or in theabsence of another finding, etc. For example, a strength of correlationor connection in the learning network 1026 can translate into apercentage or numerical score indicating a probability of correctdetection/diagnosis of the finding in the image data, a confidence inthe identification of the finding, etc.

The image data and associated finding(s) can be provided via the output1030 to be displayed, reported, logged, and/or otherwise used in anotification or alert to a healthcare practitioner such as a Tech,nurse, intensivist, trauma surgeon, etc., to act quickly on the criticaland/or other clinical finding. In some examples, the probability and/orconfidence score, and/or a criticality index/score associated with thetype of finding, size of finding, location of finding, etc., can be usedto determine a severity, degree, and/or other escalation of thealert/notification to the healthcare provider. For example, certaindetected conditions result in a text-based alert to a provider to promptthe provider for closer review. Other, more serious conditions result inan audible and/or visual alert to one or more providers for moreimmediate action. Alert(s) and/or other notification(s) can escalate inproportion to an immediacy and/or other severity of a probable detectedcondition, for example.

Image data and associated finding(s) can be provided to image enhancer1028 for image post-processing to enhance the image data. For example,the image enhancer 1028 can process the image data based on thefinding(s) to accentuate the finding(s) in a resulting image. Thus, whenthe enhanced image data is provided to the output 1030 for display(e.g., via one or more devices such as a mobile device 1040, display1042, PACS and/or other information system 1044, etc.), the finding(s)are emphasized, highlighted, noted, and/or otherwise enhanced in theresulting displayed image, for example.

By running AI on the imaging device 1010, AI findings can be leveragedto conduct enhanced image processing. For example, if the AI detectstubes/lines present in the image data, then the device software canprocess the image using an image processing technique best for viewingtubes/lines. For example, tubes and/or other lines (e.g., catheter,feeding tube, nasogastric (NG) tube, endotracheal (ET) tube, chest tube,pacemaker leads, etc.) can be emphasized or enhanced in the image datathrough an image processing algorithm that decomposes the image datainto a set of spatial frequency bands. Non-linear functions can beapplied to the frequency bands to enhance contrast and reduce noise ineach band. Spatial frequencies including tubes and lines are enhancedwhile spatial frequencies including noise are suppressed. As a result,the tubes and lines are more pronounced in a resulting image. Similarly,a pneumothorax (e.g., an abnormal collection of air in pleural spacebetween a lung and the chest), fracture, other foreign object, etc.,representing a finding can be emphasized and/or otherwise enhanced in aresulting image, for example.

The enhanced image data and associated finding(s) can be output fordisplay, storage, referral, further processing, provision to acomputer-aided diagnosis (CAD) system, etc., via the output 1030. Theoutput 1030 can provide information to a plurality of connected devices1040-1044 for review, storage, relay, and/or further action, forexample.

While example implementations are illustrated in conjunction with FIGS.1-10 , elements, processes and/or devices illustrated in conjunctionwith FIGS. 1-10 can be combined, divided, re-arranged, omitted,eliminated and/or implemented in any other way. Further, componentsdisclosed and described herein can be implemented by hardware, machinereadable instructions, software, firmware and/or any combination ofhardware, machine readable instructions, software and/or firmware. Thus,for example, components disclosed and described herein can beimplemented by analog and/or digital circuit(s), logic circuit(s),programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the components is/arehereby expressly defined to include a tangible computer readable storagedevice or storage disk such as a memory, a digital versatile disk (DVD),a compact disk (CD), a Blu-ray disk, etc. storing the software and/orfirmware.

Flowcharts representative of example machine readable instructions forimplementing components disclosed and described herein are shown inconjunction with at least FIGS. 11-12 . In the examples, the machinereadable instructions include a program for execution by a processorsuch as the processor 1312 shown in the example processor platform 1300discussed below in connection with FIG. 13 . The program may be embodiedin machine readable instructions stored on a tangible computer readablestorage medium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a Blu-ray disk, or a memory associated with theprocessor 1312, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor 1312and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchartsillustrated in conjunction with at least FIGS. 11-12 , many othermethods of implementing the components disclosed and described hereinmay alternatively be used. For example, the order of execution of theblocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Although the flowcharts of at leastFIGS. 11-12 depict example operations in an illustrated order, theseoperations are not exhaustive and are not limited to the illustratedorder. In addition, various changes and modifications may be made by oneskilled in the art within the spirit and scope of the disclosure. Forexample, blocks illustrated in the flowchart may be performed in analternative order or may be performed in parallel.

As mentioned above, the example processes of at least FIGS. 11-12 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of at least FIGS. 11-12 can beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended. In addition, the term “including” isopen-ended in the same manner as the term “comprising” is open-ended.

As shown in the example method 1100 depicted in FIG. 11 , acquired imagedata can be analyzed by the imaging device 1010 at the location of imageacquisition (e.g., patent bedside, imaging room, etc.) to evaluate imagequality and identify likely critical and/or other clinical findings totrigger further image and patient review and action. At block 1110,acquired image data acquired by the device 1010 is processed by thedevice 1010 to evaluate the quality of the image data to help ensurethat the image is of sufficient quality (e.g., good quality, otherexpected quality, etc.) for automated (e.g., machine learning, deeplearning, and/or other artificial intelligence, etc.) processing of theimage data. Image data failing to pass a quality check can be rejectedas of insufficient quality, with a notification generated to alert atechnologist and/or other user of the quality control failure. Incertain examples, artificial intelligence (AI) can be applied by theimage quality checker 1022 to analyze the image data to evaluate imagequality.

By hosting an AI algorithm on the imaging device 1010, a “quality checkAI” algorithm can be executed before a “critical condition AI” to helpensure that the image is of good quality/expected quality for the“critical condition AI” to perform well. The “quality check AI” can beused on the device as an assistant to the technologist (“Tech”) such aswhen the tech performs Quality Assurance (QA)/Quality Check (QC)practices on the images they acquire. Using AI embedded in the device1010 allows the device 1010 processor 1020 to evaluate and notify 1115the Tech if the image fails the “quality check AI”. The image fails thequality check AI, for example, if the image is of too poor quality toreliably run through a “critical condition AI” algorithm, butsimultaneously, also indicating to the Tech that perhaps the imageshould fail their manual/traditional QC activity as well, and that theTech should consider a “retake”. Thus, the image quality checker 1022can provide feedback in real-time (or substantially real-time givenimage data processing, transmission, and/or storage/retrieval latency)such as at the patient bedside via the output 1030 of the mobile x-raysystem 200, 1010 indicating/recommending via a notification 1115 that animage should be re-acquired, for example. For example, the notification1115 can be provided via an overlay on the mobile device 1040, display1042, etc., to show localization (e.g., via a heatmap, etc.) of the AIfinding and/or associated information.

Thus, rather than relying on a Tech's manual assessment, the qualitychecker 1022 can leverage AI and/or other processing to analyze imageanatomy, orientation/position, sufficient contrast, appropriate dose,too much noise/artifacts, etc., to evaluate image quality andsufficiency to enable further automated analysis.

If image quality is sufficient and/or otherwise appropriate (e.g.,correct view/position, correct anatomy, acceptable patient positioning,contrast and/or noise level, etc.) for analysis, then, at block 1120,the image data is pre-processed to prepare the image data for clinicalanalysis. For example, the image data can be conditioned for processingby machine learning, such as a deep learning network, etc., to identifyone or more features of interest in the image data. The pre-processor1024 can apply techniques such as image segmentation to identify anddivide different regions or areas in the image, for example. Thepre-processor 1024 can apply techniques such as cropping to select acertain region of interest in the image for further processing andanalysis, for example. The pre-processor 1024 can apply techniques suchas down-sampling, anatomical segmentation, normalizing with mean and/orstandard deviation of training population, contrast enhancement, etc.,to scale or reduce image data size for further processing (e.g., bypresenting the learning network 1026 with fewer samples representing theimage data, etc.), for example.

At block 1130, the pre-processed image data is provided to the learningnetwork 1026 for processing of the image data to identify one or moreclinical/critical findings. As discussed above, the learning network1026, such as a deep learning network, other CNN and/or other machinelearning network, etc., receives the pre-processed image data at itsinput nodes and evaluates the image data according to the nodes andconnective pathways of the learning network 1026 to correlate featuresidentified in the pre-processed image data with critical and/or otherclinical findings. Based on image intensity values, reference coordinateposition, proximity, and/or other characteristics, items determined inthe image data can be correlated with likely critical and/or otherclinical findings such as a severe pneumothorax, tube within the rightmainstem, free air in the bowel, etc.

For example, a large, highly curated set of X-Ray images can be used totrain a deep convolution network (e.g., the example network of FIGS. 3-5, etc.) including several layers in an offline compute-intensiveenvironment. The network is trained to output classification labelsdepicting a detected pathology and are able to extract features that canlocalize and bound regions interest to the detected pathology. Thespecialized network is developed and trained to output quantificationmetrics such as fluid density, opacity and volumetric measurements, etc.As shown in the example of FIGS. 6A-9C, trained model(s) are deployedonto an X-Ray device (e.g., the imaging device 10, 200, 1010, etc.)which is either mobile or installed in a fixed X-Ray room. The processor1020 leverages the trained, deployed model(s) to infer properties,features, and/or other aspects of the image data by inputting the X-Rayimage into the trained network model(s). The deployed model(s) helpcheck quality and suitability of the image for inference via the imagequality checker 1022 and infer findings via the learning network 1026,for example. The images can be pre-processed in real time based onacquisition conditions that generated the image to improve accuracy andefficacy of the inference process. In certain examples, the learningnetwork(s) 1026 are trained, updated, and redeployed continuously and/orperiodically upon acquisition of additional curated data. As a result,more accurate and feature enhanced networks are deployed on the imagingdevice 1010.

In certain examples, a probability and/or confidence indicator or scorecan be associated with the indication of critical and/or other clinicalfinding(s), as well as a size of the finding, location of the finding,severity of the finding, etc. For example, a strength of correlation orconnection in the learning network 1026 can translate into a percentageor numerical score indicating a probability of correctdetection/diagnosis of the finding in the image data, a confidence inthe identification of the finding, etc.

The image data and associated finding(s) can be provided via the output1030 to be displayed, reported, logged, and/or otherwise used in anotification or alert 1135 to a healthcare practitioner such as a Tech,nurse, intensivist, trauma surgeon, and/or clinical system, etc., to actquickly on the critical and/or other clinical finding. In some examples,the probability and/or confidence score, and/or a criticalityindex/score associated with the type of finding, can be used todetermine a severity, degree, and/or other escalation of thealert/notification to the healthcare provider. For example, certaindetected conditions result in a text-based alert to a provider to promptthe provider for closer review. Other, more serious conditions result inan audible and/or visual alert to one or more providers for moreimmediate action. Alert(s) and/or other notification(s) can escalate inproportion to an immediacy and/or other severity of a probable detectedcondition, for example.

At block 1140, image data is enhanced based on associated finding(s)identified by the learning network 1026. For example, the image enhancer1028 can process the image data based on the finding(s) to accentuatethe finding(s) in a resulting image. Thus, when the enhanced image datais provided to the output 1030 for display (e.g., via one or moredevices such as a mobile device 1040, display 1042, PACS and/or otherinformation system 1044, etc.), the finding(s) are emphasized,highlighted, noted, and/or otherwise enhanced in the resulting displayedimage, for example.

By running AI on the imaging device 1010, AI findings can be leveragedto conduct enhanced image processing. For example, if the AI detectstubes/lines present in the image data, then the device software canprocess the image using an image processing technique best for viewingtubes/lines.

The enhanced image data and associated finding(s) can be output fordisplay, storage, referral, further processing, provision to acomputer-aided diagnosis (CAD) system, etc., via the output 1030. Theoutput 1030 can provide information to a plurality of connected devices1040-1044 for review, storage, relay, and/or further action, forexample. As shown in the example of FIG. 11 , enhanced image data andassociated finding(s) can be output for display on a device 1150 (e.g.,a handheld or mobile device, etc.), displayed on a workstation 1152(e.g., an information system, a display associated with the imagingdevice 1010, etc.), and/or sent to a clinical information system such asa PACS, RIS, enterprise archive, etc., for storage and/or furtherprocessing 1154.

FIG. 12 illustrates a flow diagram for example implementation ofchecking image quality (1110) and applying artificial intelligence(1130) to image data to determine critical and/or other clinicalfindings in the image data.

Portable, real-time, at point of patient care, at point of imageacquisition, dynamic determination and prompting for further action,integrated into imaging device. At 1202, image data, such as DICOM imagedata, is provided from a mobile x-ray imaging device (e.g., the device200 and/or 1010, etc.). At 1204, metadata associated with the image data(e.g., DICOM header information, other associated metadata, etc.) isanalyzed to determine whether the image data matches a position andregion indicated by the metadata. For example, if the DICOM metadataindicates that the image is a frontal (e.g., anteroposterior (AP) orposteroanterior (PA)) chest image, then an analysis of the image datashould confirm that position (e.g., location and orientation, etc.). Ifthe image does not match its indicated position and region, then, at1206, a notification, alert, and/or warning is generated indicating thatthe image is potentially improper. The warning can be an audible,visual, and/or system alert or other notification, for example, and canprompt a user for further action (e.g., re-acquire the image data,etc.), trigger a system for further action, log the potential error,etc.

If the image data appears to match its prescribed position and region,then, at 1208, the image data is analyzed to determine whether the imagepasses image quality control check(s). For example, the image data isanalyzed to determine whether the associated image has good patientpositioning (e.g., the patient is positioned such that an anatomy orregion of interested is centered in the image, etc.). Other qualitycontrol checks can include an evaluation of sufficient contrast, ananalysis of a level of noise or artifact in the image, an examination ofappropriate/sufficient dosage for image clarity, etc.

If the image fails a quality control check, then, at 1210, a warning ofcompromised image quality is generated. For example, a user, othersystem, etc., can receive an alert and/or other notification (e.g., avisual and/or audible alert on screen, via message, log notation,trigger, etc.) that the image quality may not be sufficient and/or maypresent issues in evaluating the image data to determine clinicalfinding(s). At 1212, settings and/or other input is evaluated todetermine whether to proceed with further image processing. For example,user input in response to the notification can indicate whether or notto proceed anyway, and/or a configuration setting, etc., can specify adefault instruction or threshold regarding whether or not to proceedwith further image analysis despite image quality concerns. If theinstruction is not to proceed, then the process 1200 ends.

If analysis is to proceed (e.g., because the image passes qualitycheck(s) and/or an instruction indicates to proceed despite imagequality concerns, etc.), then, at 1214, the image data is evaluated withrespect to a clinical check. For example, a deep learning network,machine learning, and/or other AI is applied to analyze the image datato detect the presence of a critical and/or other clinical finding. Forexample, image data can be processed by the learning network 1026 toidentify a severe pneumothorax and/or other condition (e.g., tube withinthe right mainstem, free air in the bowel, fracture, tumor, lesion,other foreign object, etc.) in the image data. If no finding isdetermined, then the process 1200 ends.

If, however, a finding is determined, then, at 1216, a finding alertand/or other notification is generated. For example, a critical findingalert is generated based on the identification of a pneumothorax,incorrect position of an ET tube, position of tube in right main stem,etc. The alert can be generated in proportion to and/or othercorrelation with a severity/urgency of the clinical finding, confidencein the finding, type of finding, location of the finding, and/orappearance of the finding in conjunction with another finding or inabsence of another finding, for example. For example, a critical findingcan be alerted more urgently to a healthcare practitioner and/or otheruser than a less-critical clinical finding. On-screen alert(s) can be13-displayed, HL7 messages can be provided to the RIS, etc. In certainexamples, image data can be re-processed such as by the image enhancer1028 to more optimally display the finding(s) to a user.

FIGS. 13-20 illustrate example displays to provide output and facilitateinteraction including on-screen alerts, indicators, notifications, etc.,in accordance with the apparatus, systems, and methods described abovein connection with FIGS. 1-12 . The example displays can be provided viathe imaging devices 10, 200, 1010, and/or a separate handheld or mobilecomputing device, workstation, etc.

FIG. 13 shows an example graphical user interface (GUI) 1300 including adeviation index (DI) 1310 (e.g., an indication of correct imageacquisition technique with 0.0 being a perfect exposure) and anindication of priority 1320 (e.g., from processing by the system 1000including AI). As shown in the example of FIG. 13 , the priorityindication 1320 is high 1322. FIG. 14 illustrates the example GUI 1300with a priority indication 1320 of medium 1324. FIG. 15 illustrates theexample GUI 1300 with a priority indication 1310 of low 1326.

FIG. 16 illustrates an example GUI 1600 including a DI 1610, a qualitycontrol indicator 1620 (e.g., pass or fail for acceptable quality,etc.), a criticality index 1630 (e.g., normal, abnormal, critical,etc.), a criticality value 1635 associated with the criticality index1630, an indication of finding 1640 (e.g., mass, fracture, pneumothorax,etc.), and an indication of size or severity 1650 (e.g., small, medium,large, etc.). Thus, a user can interact with the example GUI 1600 andevaluate the DI 1610, quality indication 1620, criticality range 1630and value 1635 for clinical impression, type of finding 1640, andseverity of finding 1650.

FIG. 17 illustrates another example GUI 1700, similar to but reducedfrom the example GUI 1600, including a DI 1710, a criticality impression1720, an indication of finding 1730, and an indication of severity 1740.FIG. 18 illustrates an example GUI 1800 similar to the example GUI 1700further including an overlay 1810 of the finding on the image.

FIG. 19 illustrates an example GUI 1900 providing potential findingsfrom AI in a window 1910 overlaid on the image viewer display of the GUI1900. FIG. 20 illustrates another view of the example GUI 1900 in whichentries 2002, 2004 in the AI findings window 1910 have been expanded toreveal further information regarding the respective finding 2002, 2004.FIG. 21 illustrates a further view of the example GUI 1900 in which theAI findings window 1910 has been reduced to a miniature representation2110, selectable to view information regarding the findings.

FIG. 22 illustrates an example GUI 2200 in which a finding 2210 ishighlighted on an associated image, and related information 2220 is alsodisplayed. FIG. 23 illustrates an example configuration interface 2300to configure AI to process image and/or other data and generatefindings.

FIG. 24 illustrates a first example abbreviated GUI 2400 (e.g., aweb-based GUI, etc.) displayable on a smartphone 2410 and/or othercomputing device, and a second abbreviated GUI 2420 shown on a tablet2430. As shown in the example of FIG. 24 , the tablet 2430 can bemounted with respect to the imaging device 10, 200, 1010 for viewing andinteraction by an x-ray technician and/or other healthcare practitioner,for example.

FIG. 25 is a block diagram of an example processor platform 2500structured to executing the instructions of at least FIGS. 11-12 toimplement the example components disclosed and described herein. Theprocessor platform 2500 can be, for example, a server, a personalcomputer, a mobile device (e.g., a cell phone, a smart phone, a tabletsuch as an iPad™), a personal digital assistant (PDA), an Internetappliance, or any other type of computing device.

The processor platform 2500 of the illustrated example includes aprocessor 2512. The processor 2512 of the illustrated example ishardware. For example, the processor 2512 can be implemented byintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 2512 of the illustrated example includes a local memory2513 (e.g., a cache). The example processor 2512 of FIG. 25 executes theinstructions of at least FIGS. 11-12 to implement the systems,infrastructure, displays, and associated methods of FIGS. 1-24 such asthe image quality checker 1022, the pre-processor 1024, the learningnetwork 1026, the image enhancer 1028, and the output 1030 of theprocessor 1020/2512, etc. The processor 2512 of the illustrated exampleis in communication with a main memory including a volatile memory 2514and a non-volatile memory 2516 via a bus 2518. The volatile memory 2514may be implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 2516 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 2514,2516 is controlled by a clock controller.

The processor platform 2500 of the illustrated example also includes aninterface circuit 2520. The interface circuit 2520 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 2522 are connectedto the interface circuit 2520. The input device(s) 2522 permit(s) a userto enter data and commands into the processor 2512. The input device(s)can be implemented by, for example, a sensor, a microphone, a camera(still or video, RGB or depth, etc.), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 2524 are also connected to the interfacecircuit 2520 of the illustrated example. The output devices 2524 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, and/or speakers). The interface circuit 2520 of theillustrated example, thus, typically includes a graphics driver card, agraphics driver chip or a graphics driver processor.

The interface circuit 2520 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network2526 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 2500 of the illustrated example also includes oneor more mass storage devices 2528 for storing software and/or data.Examples of such mass storage devices 2528 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 2532 of FIG. 25 may be stored in the mass storagedevice 2528, in the volatile memory 2514, in the non-volatile memory2516, and/or on a removable tangible computer readable storage mediumsuch as a CD or DVD.

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus, and articles of manufacture have been disclosed tomonitor, process, and improve operation of imaging and/or otherhealthcare systems using a plurality of deep learning and/or othermachine learning techniques.

Thus, certain examples facilitate image acquisition and analysis at thepoint of care such as via a portable imaging device at the point ofpatient imaging. If images should be re-taken, further analysis doneright away, and/or other criticality explored sooner, rather than later,the example systems, apparatus, and methods disclosed and describedherein can facilitate such action to automate analysis, streamlineworkflow, and improve patient care.

Certain examples provide a specially-configured imaging apparatus thatcan acquire images and operate as a decision support tool at the pointof care for a critical care team. Certain examples provide an imagingapparatus that functions as a medical device to provide and/orfacilitate diagnosis at the point of care to detect radiologicalfindings, etc. The apparatus can trigger a critical alert for aradiologist and/or critical care team to bring immediate attention tothe patient. The apparatus enables patient triaging after the patient'sexam, such as in a screening environment, wherein negative tests allowthe patient to return home, while a positive test would require thepatient to be seen by a physician before returning home

In certain examples, a mobile device and/or cloud product enables avendor-neutral solution, proving point of care alerts on any digitalx-ray system (e.g., fully integrated, upgrade kit, etc.). In certainexamples, embedded AI algorithms executing on a mobile imaging system,such as a mobile x-ray machine, etc., provide point of care alertsduring and/or in real-time following image acquisition, etc.

By hosting AI on the imaging device, the mobile x-ray system can be usedin rural regions without hospital information technology networks, oreven on a mobile truck that brings imaging to patient communities, forexample. Additionally, if there is long latency to send an image to aserver or cloud, AI on the imaging device can instead be executed andgenerate output back to the imaging device for further action. Ratherthan having the x-ray technologist moved onto the next patient and thex-ray device no longer at the patient's bedside with the clinical careteam, image processing, analysis, and output can occur in real time (orsubstantially real time given some data transfer/retrieval, processing,and output latency) to provide a relevant notification to the clinicalcare team while they and the equipment are still with or near thepatient. For trauma cases, for example, treatment decisions need to bemade fast, and certain examples alleviate the delay found with otherclinical decision support tools.

Mobile X-ray systems travel throughout the hospital to the patientbedside (e.g., emergency room, operating room, intensive care unit, etc.Within a hospital, network communication may be unreliable in “dead”zones of the hospital (e.g., basement, rooms with electrical signalinterference or blockage, etc.). If the X-ray device relies on buildingWi-Fi, for example, to push the image to a server or cloud which ishosting the AI model and then wait to receive the AI output back to theX-ray device, then patient is at risk of not having reliability incritical alerts when needed. Further, if a network or power outageimpacts communications, the AI operating on the imaging device cancontinue to function as a self-contained, mobile processing unit.

Examples of alerts generated for general radiology can include criticalalerts (e.g., for mobile x-ray, etc.) such as pneumothorax, tubes andline placement, pleural effusion, lobar collapse, pneumoperitoneum,pneumonia, etc.; screening alerts (e.g., for fixed x-ray, etc.) such astuberculosis, lung nodules, etc.; quality alerts (e.g., for mobileand/or fixed x-ray, etc.) such as patient positioning, clipped anatomy,inadequate technique, image artifacts, etc.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A mobile imaging apparatus comprising: wheels; asource; a detector; and a housing including: an image data store tostore image data acquired at a location using the mobile imagingapparatus; and at least one processor to facilitate acquisition of theimage data and to process the image data acquired at the locationaccording to an image quality measure, the image quality measureselected based on an analysis of at least one of digital imaging andcommunication in medicine (DICOM) header information or other DICOMmetadata, the at least one processor to implement: a trained learningnetwork to process, when the image data satisfies the selected imagequality measure, the image data while at the location to identify aclinical finding in the image data, the identification of a clinicalfinding to trigger a notification at the mobile imaging apparatus via anoutput to notify a healthcare practitioner regarding the clinicalfinding and prompt a responsive action with respect to a patientassociated with the image data.
 2. The apparatus of claim 1, wherein theat least one processor is to implement an image quality checker toevaluate the image data from the image data store in comparison to theimage quality measure using an artificial intelligence model operatingon the mobile imaging apparatus at the location.
 3. The apparatus ofclaim 2, wherein the image quality checker selects the image qualitymeasure based on the analysis of the at least one of the DICOM headerinformation or other DICOM metadata.
 4. The apparatus of claim 1,wherein the image quality measure includes analysis of at least one ofDICOM header information or other DICOM metadata to confirm at least oneof protocol or anatomy in the image data.
 5. The apparatus of claim 1,wherein the mobile imaging apparatus is a mobile x-ray imagingapparatus.
 6. The apparatus of claim 1, wherein the output includes adisplay, wherein the notification is output via a graphical userinterface displayed via the display at the mobile imaging apparatus. 7.The apparatus of claim 6, wherein presentation of the notificationcorresponds to a criticality of the clinical finding.
 8. The apparatusof claim 1, wherein the trained learning network includes a deeplearning network trained on reference image data and associated findingsand deployed to the mobile imaging apparatus.
 9. The apparatus of claim1, wherein the at least one processor is to implement a pre-processor toprepare the image data for processing by the trained learning networkafter evaluation according to the image quality measure.
 10. Theapparatus of claim 1, wherein the at least one processor is to implementan image enhancer to process the image data to enhance the clinicalfinding in a displayed image resulting from the image data.
 11. Anon-transitory computer-readable storage medium in a mobile imagingapparatus including wheels, a source, a detector, and a housingincluding the computer-readable storage medium and at least oneprocessor, the computer-readable storage medium to store image dataacquired at a location using the mobile imaging apparatus and to includeinstructions which, when executed, cause the at least one processor inthe mobile imaging apparatus to at least facilitate acquisition of theimage data and to process the image data acquired at the locationaccording to an image quality measure, the image quality measureselected based on an analysis of at least one of digital imaging andcommunication in medicine (DICOM) header information or other DICOMmetadata, the instructions, when executed, to cause the at least oneprocessor to implement a trained learning network to process, when theimage data satisfies the selected image quality measure, the image datawhile at the location to identify a clinical finding in the image data,the identification of a clinical finding to trigger a notification atthe mobile imaging apparatus via an output to notify a healthcarepractitioner regarding the clinical finding and prompt a responsiveaction with respect to a patient associated with the image data.
 12. Thenon-transitory computer-readable storage medium of claim 11, wherein theinstructions, when executed, cause the at least one processor toimplement an image quality checker to evaluate the image data incomparison to the image quality measure using an artificial intelligencemodel operating on the mobile imaging apparatus at the location.
 13. Thenon-transitory computer-readable storage medium of claim 12, wherein theimage quality checker selects the image quality measure based on theanalysis of the at least one of the DICOM header information or otherDICOM metadata.
 14. The non-transitory computer-readable storage mediumof claim 11, wherein the image quality measure includes analysis of atleast one of DICOM header information or other DICOM metadata to confirmat least one of protocol or anatomy in the image data.
 15. Thenon-transitory computer-readable storage medium of claim 11, wherein themobile imaging apparatus is a mobile x-ray imaging apparatus.
 16. Thenon-transitory computer-readable storage medium of claim 11, wherein theoutput includes a display, wherein the notification is output via agraphical user interface displayed via the display at the mobile imagingapparatus.
 17. The non-transitory computer-readable storage medium ofclaim 16, wherein presentation of the notification corresponds to acriticality of the clinical finding.
 18. The non-transitorycomputer-readable storage medium of claim 11, wherein the trainedlearning network includes a deep learning network trained on referenceimage data and associated findings and deployed to the mobile imagingapparatus.
 19. The non-transitory computer-readable storage medium ofclaim 11, wherein the instructions, when executed, cause the at leastone processor to implement a pre-processor to prepare the image data forprocessing by the trained learning network after evaluation according tothe image quality measure.
 20. The non-transitory computer-readablestorage medium of claim 11, wherein the instructions, when executed,cause the at least one processor to implement an image enhancer toprocess the image data to enhance the clinical finding in a displayedimage resulting from the image data.